Learning with Feature-Dependent Label Noise: A Progressive Approach

Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, Chao Chen

Keywords: Noisy Label, Deep Learning, Classification

Abstract: Label noise is frequently observed in real-world large-scale datasets. The noise is introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most existing approaches to handling noisy labels fall into two categories: they either assume an ideal feature-independent noise, or remain heuristic without theoretical guarantees. In this paper, we propose to target a new family of feature-dependent label noise, which is much more general than commonly used i.i.d. label noise and encompasses a broad spectrum of noise patterns. Focusing on this general noise family, we propose a progressive label correction algorithm that iteratively corrects labels and refines the model. We provide theoretical guarantees showing that for a wide variety of (unknown) noise patterns, a classifier trained with this strategy converges to be consistent with the Bayes classifier. In experiments, our method outperforms SOTA baselines and is robust to various noise types and levels.

One-sentence Summary: We propose a progressive label correction approach for noisy label learning task.

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Code: [ pxiangwu/PLC](https://github.com/pxiangwu/PLC)

Data: [ANIMAL](https://paperswithcode.com/dataset/animal), [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [CIFAR-100](https://paperswithcode.com/dataset/cifar-100), [Clothing1M](https://paperswithcode.com/dataset/clothing1m), [Food-101](https://paperswithcode.com/dataset/food-101)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2103.07756/code)